Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations686
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.4 KiB
Average record size in memory96.2 B

Variable types

Numeric8
Categorical4

Alerts

Unnamed: 0 is highly overall correlated with er and 1 other fieldsHigh correlation
age is highly overall correlated with menoHigh correlation
er is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
meno is highly overall correlated with ageHigh correlation
pgr is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
pid has unique values Unique
pgr has 88 (12.8%) zeros Zeros
er has 82 (12.0%) zeros Zeros

Reproduction

Analysis started2025-02-08 03:21:28.197122
Analysis finished2025-02-08 03:21:52.984378
Duration24.79 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct686
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343.5
Minimum1
Maximum686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:21:53.217635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.25
Q1172.25
median343.5
Q3514.75
95-th percentile651.75
Maximum686
Range685
Interquartile range (IQR)342.5

Descriptive statistics

Standard deviation198.17543
Coefficient of variation (CV)0.57692992
Kurtosis-1.2
Mean343.5
Median Absolute Deviation (MAD)171.5
Skewness0
Sum235641
Variance39273.5
MonotonicityStrictly increasing
2025-02-07T19:21:53.752370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
461 1
 
0.1%
453 1
 
0.1%
454 1
 
0.1%
455 1
 
0.1%
456 1
 
0.1%
457 1
 
0.1%
458 1
 
0.1%
459 1
 
0.1%
460 1
 
0.1%
Other values (676) 676
98.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
686 1
0.1%
685 1
0.1%
684 1
0.1%
683 1
0.1%
682 1
0.1%
681 1
0.1%
680 1
0.1%
679 1
0.1%
678 1
0.1%
677 1
0.1%

pid
Real number (ℝ)

Unique 

Distinct686
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean966.06122
Minimum1
Maximum1819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:21:54.205332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.5
Q1580.75
median1015.5
Q31340.5
95-th percentile1704
Maximum1819
Range1818
Interquartile range (IQR)759.75

Descriptive statistics

Standard deviation495.50625
Coefficient of variation (CV)0.51291392
Kurtosis-0.98126153
Mean966.06122
Median Absolute Deviation (MAD)366
Skewness-0.22237267
Sum662718
Variance245526.44
MonotonicityNot monotonic
2025-02-07T19:21:54.635246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 1
 
0.1%
1735 1
 
0.1%
670 1
 
0.1%
1224 1
 
0.1%
1139 1
 
0.1%
933 1
 
0.1%
1363 1
 
0.1%
1088 1
 
0.1%
386 1
 
0.1%
1746 1
 
0.1%
Other values (676) 676
98.5%
ValueCountFrequency (%)
1 1
0.1%
6 1
0.1%
13 1
0.1%
37 1
0.1%
38 1
0.1%
39 1
0.1%
44 1
0.1%
48 1
0.1%
51 1
0.1%
52 1
0.1%
ValueCountFrequency (%)
1819 1
0.1%
1818 1
0.1%
1817 1
0.1%
1816 1
0.1%
1810 1
0.1%
1809 1
0.1%
1808 1
0.1%
1807 1
0.1%
1796 1
0.1%
1794 1
0.1%

age
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.052478
Minimum21
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:21:55.067721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile36
Q146
median53
Q361
95-th percentile68
Maximum80
Range59
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.120739
Coefficient of variation (CV)0.19076845
Kurtosis-0.3628573
Mean53.052478
Median Absolute Deviation (MAD)7
Skewness-0.14610191
Sum36394
Variance102.42936
MonotonicityNot monotonic
2025-02-07T19:21:55.437802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 36
 
5.2%
51 29
 
4.2%
46 28
 
4.1%
62 26
 
3.8%
63 26
 
3.8%
49 26
 
3.8%
65 26
 
3.8%
48 25
 
3.6%
59 25
 
3.6%
53 25
 
3.6%
Other values (44) 414
60.3%
ValueCountFrequency (%)
21 1
 
0.1%
25 2
 
0.3%
27 1
 
0.1%
29 2
 
0.3%
30 1
 
0.1%
31 4
0.6%
32 6
0.9%
33 6
0.9%
34 6
0.9%
35 3
0.4%
ValueCountFrequency (%)
80 2
 
0.3%
79 1
 
0.1%
77 1
 
0.1%
76 1
 
0.1%
75 2
 
0.3%
74 3
 
0.4%
73 1
 
0.1%
72 2
 
0.3%
71 4
0.6%
70 9
1.3%

meno
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
1
396 
0
290 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters686
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 396
57.7%
0 290
42.3%

Length

2025-02-07T19:21:55.828098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:21:56.177893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 396
57.7%
0 290
42.3%

Most occurring characters

ValueCountFrequency (%)
1 396
57.7%
0 290
42.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 686
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 396
57.7%
0 290
42.3%

Most occurring scripts

ValueCountFrequency (%)
Common 686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 396
57.7%
0 290
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 396
57.7%
0 290
42.3%

size
Real number (ℝ)

Distinct58
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.329446
Minimum3
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:21:56.496893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12
Q120
median25
Q335
95-th percentile57
Maximum120
Range117
Interquartile range (IQR)15

Descriptive statistics

Standard deviation14.296217
Coefficient of variation (CV)0.48743563
Kurtosis5.3248343
Mean29.329446
Median Absolute Deviation (MAD)6
Skewness1.7757762
Sum20120
Variance204.38182
MonotonicityNot monotonic
2025-02-07T19:21:56.936897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 68
 
9.9%
25 66
 
9.6%
35 47
 
6.9%
20 45
 
6.6%
22 31
 
4.5%
15 31
 
4.5%
40 30
 
4.4%
21 29
 
4.2%
23 28
 
4.1%
18 21
 
3.1%
Other values (48) 290
42.3%
ValueCountFrequency (%)
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
7 2
 
0.3%
8 4
 
0.6%
9 1
 
0.1%
10 8
1.2%
11 4
 
0.6%
12 15
2.2%
13 7
1.0%
ValueCountFrequency (%)
120 1
 
0.1%
100 3
 
0.4%
80 4
 
0.6%
78 1
 
0.1%
75 1
 
0.1%
70 7
1.0%
65 2
 
0.3%
61 1
 
0.1%
60 11
1.6%
58 3
 
0.4%

grade
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
2
444 
3
161 
1
81 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters686
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 444
64.7%
3 161
 
23.5%
1 81
 
11.8%

Length

2025-02-07T19:21:57.325350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:21:57.672360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 444
64.7%
3 161
 
23.5%
1 81
 
11.8%

Most occurring characters

ValueCountFrequency (%)
2 444
64.7%
3 161
 
23.5%
1 81
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 686
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 444
64.7%
3 161
 
23.5%
1 81
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 444
64.7%
3 161
 
23.5%
1 81
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 444
64.7%
3 161
 
23.5%
1 81
 
11.8%

nodes
Real number (ℝ)

Distinct30
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0102041
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:21:57.989364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile16
Maximum51
Range50
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.4754833
Coefficient of variation (CV)1.0928663
Kurtosis13.313425
Mean5.0102041
Median Absolute Deviation (MAD)2
Skewness2.8847593
Sum3437
Variance29.980918
MonotonicityNot monotonic
2025-02-07T19:21:58.828898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 187
27.3%
2 110
16.0%
3 79
11.5%
4 57
 
8.3%
5 41
 
6.0%
7 36
 
5.2%
6 33
 
4.8%
8 20
 
2.9%
9 20
 
2.9%
10 19
 
2.8%
Other values (20) 84
12.2%
ValueCountFrequency (%)
1 187
27.3%
2 110
16.0%
3 79
11.5%
4 57
 
8.3%
5 41
 
6.0%
6 33
 
4.8%
7 36
 
5.2%
8 20
 
2.9%
9 20
 
2.9%
10 19
 
2.8%
ValueCountFrequency (%)
51 1
0.1%
38 1
0.1%
36 1
0.1%
35 1
0.1%
33 1
0.1%
30 1
0.1%
26 1
0.1%
24 2
0.3%
23 1
0.1%
21 1
0.1%

pgr
Real number (ℝ)

High correlation  Zeros 

Distinct242
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.99563
Minimum0
Maximum2380
Zeros88
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:21:59.249085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median32.5
Q3131.75
95-th percentile408
Maximum2380
Range2380
Interquartile range (IQR)124.75

Descriptive statistics

Standard deviation202.33155
Coefficient of variation (CV)1.8394509
Kurtosis35.07325
Mean109.99563
Median Absolute Deviation (MAD)32.5
Skewness4.7863171
Sum75457
Variance40938.057
MonotonicityIncreasing
2025-02-07T19:21:59.655863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 88
 
12.8%
1 24
 
3.5%
2 17
 
2.5%
6 13
 
1.9%
8 13
 
1.9%
10 12
 
1.7%
26 12
 
1.7%
5 11
 
1.6%
7 10
 
1.5%
11 10
 
1.5%
Other values (232) 476
69.4%
ValueCountFrequency (%)
0 88
12.8%
1 24
 
3.5%
2 17
 
2.5%
3 9
 
1.3%
4 6
 
0.9%
5 11
 
1.6%
6 13
 
1.9%
7 10
 
1.5%
8 13
 
1.9%
9 8
 
1.2%
ValueCountFrequency (%)
2380 1
0.1%
1600 1
0.1%
1490 1
0.1%
1356 1
0.1%
1152 1
0.1%
1118 1
0.1%
980 1
0.1%
935 1
0.1%
912 1
0.1%
860 1
0.1%

er
Real number (ℝ)

High correlation  Zeros 

Distinct244
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.252187
Minimum0
Maximum1144
Zeros82
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:22:00.073086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median36
Q3114
95-th percentile384
Maximum1144
Range1144
Interquartile range (IQR)106

Descriptive statistics

Standard deviation153.08396
Coefficient of variation (CV)1.5904466
Kurtosis12.50526
Mean96.252187
Median Absolute Deviation (MAD)35
Skewness3.0877652
Sum66029
Variance23434.7
MonotonicityNot monotonic
2025-02-07T19:22:00.470619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82
 
12.0%
2 18
 
2.6%
8 17
 
2.5%
1 16
 
2.3%
5 14
 
2.0%
11 14
 
2.0%
4 12
 
1.7%
3 12
 
1.7%
10 10
 
1.5%
9 10
 
1.5%
Other values (234) 481
70.1%
ValueCountFrequency (%)
0 82
12.0%
1 16
 
2.3%
2 18
 
2.6%
3 12
 
1.7%
4 12
 
1.7%
5 14
 
2.0%
6 5
 
0.7%
7 3
 
0.4%
8 17
 
2.5%
9 10
 
1.5%
ValueCountFrequency (%)
1144 1
0.1%
1091 1
0.1%
1060 1
0.1%
972 1
0.1%
898 1
0.1%
792 1
0.1%
753 1
0.1%
749 1
0.1%
701 1
0.1%
700 1
0.1%

hormon
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
0
440 
1
246 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters686
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 440
64.1%
1 246
35.9%

Length

2025-02-07T19:22:00.825108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:22:01.219292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 440
64.1%
1 246
35.9%

Most occurring characters

ValueCountFrequency (%)
0 440
64.1%
1 246
35.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 686
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 440
64.1%
1 246
35.9%

Most occurring scripts

ValueCountFrequency (%)
Common 686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 440
64.1%
1 246
35.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 440
64.1%
1 246
35.9%

rfstime
Real number (ℝ)

Distinct574
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1124.4898
Minimum8
Maximum2659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2025-02-07T19:22:01.578433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile195
Q1567.75
median1084
Q31684.75
95-th percentile2194.25
Maximum2659
Range2651
Interquartile range (IQR)1117

Descriptive statistics

Standard deviation642.79195
Coefficient of variation (CV)0.57162986
Kurtosis-0.98899764
Mean1124.4898
Median Absolute Deviation (MAD)540
Skewness0.26385832
Sum771400
Variance413181.49
MonotonicityNot monotonic
2025-02-07T19:22:02.048277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
548 3
 
0.4%
859 3
 
0.4%
2372 3
 
0.4%
855 3
 
0.4%
338 3
 
0.4%
1499 3
 
0.4%
740 3
 
0.4%
491 3
 
0.4%
1722 3
 
0.4%
1701 3
 
0.4%
Other values (564) 656
95.6%
ValueCountFrequency (%)
8 1
0.1%
15 1
0.1%
16 1
0.1%
17 2
0.3%
18 1
0.1%
29 1
0.1%
42 1
0.1%
46 1
0.1%
57 1
0.1%
63 1
0.1%
ValueCountFrequency (%)
2659 1
0.1%
2612 1
0.1%
2563 1
0.1%
2556 1
0.1%
2551 1
0.1%
2539 1
0.1%
2471 1
0.1%
2467 1
0.1%
2456 2
0.3%
2449 1
0.1%

status
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.0 KiB
0
387 
1
299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters686
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 387
56.4%
1 299
43.6%

Length

2025-02-07T19:22:02.576228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T19:22:02.962591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 387
56.4%
1 299
43.6%

Most occurring characters

ValueCountFrequency (%)
0 387
56.4%
1 299
43.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 686
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 387
56.4%
1 299
43.6%

Most occurring scripts

ValueCountFrequency (%)
Common 686
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 387
56.4%
1 299
43.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 387
56.4%
1 299
43.6%

Interactions

2025-02-07T19:21:49.353024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:29.227618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:32.145789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:35.697868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:38.453773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:41.112644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:43.812078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:46.623735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:49.757100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:29.625459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:32.527515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:36.051614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:38.790776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:41.466892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:44.146915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:46.997243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:50.120399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:29.980255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:32.912310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:36.388328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:39.145433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:41.810040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:44.531207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:47.336238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:50.446299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:30.328348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:33.338342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:36.715883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:39.476608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:42.146730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:44.889284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:47.657236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:50.770299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:30.727270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:33.708336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:37.139921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:39.801565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:42.505634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:45.273618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:48.021826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:51.082382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:31.113368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:34.078364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:37.468025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:40.133292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:42.831241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:45.617100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:48.344821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:51.372899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:31.428241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:34.477430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:37.776031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:40.464317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:43.137791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:45.951717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:48.663775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:51.715168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:31.787228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:34.959486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:38.122766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:40.791967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:43.486208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:46.274468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-02-07T19:21:49.011684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-02-07T19:22:03.226608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0ageergradehormonmenonodespgrpidrfstimesizestatus
Unnamed: 01.0000.0210.5970.2740.0000.000-0.1110.9990.0320.261-0.0540.209
age0.0211.0000.2600.0840.2660.819-0.0020.023-0.0050.044-0.0410.137
er0.5970.2601.0000.0690.1220.297-0.0930.5980.0090.161-0.0360.060
grade0.2740.0840.0691.0000.0290.0000.0800.1230.0000.0870.0320.151
hormon0.0000.2660.1220.0291.0000.2710.0000.0090.1260.1330.0970.068
meno0.0000.8190.2970.0000.2711.0000.0000.0000.0090.0660.0560.015
nodes-0.111-0.002-0.0930.0800.0000.0001.000-0.1130.041-0.2580.2720.238
pgr0.9990.0230.5980.1230.0090.000-0.1131.0000.0300.262-0.0570.082
pid0.032-0.0050.0090.0000.1260.0090.0410.0301.000-0.2560.0730.161
rfstime0.2610.0440.1610.0870.1330.066-0.2580.262-0.2561.000-0.1490.481
size-0.054-0.041-0.0360.0320.0970.0560.272-0.0570.073-0.1491.0000.142
status0.2090.1370.0600.1510.0680.0150.2380.0820.1610.4810.1421.000

Missing values

2025-02-07T19:21:52.151781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-07T19:21:52.755150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0pidagemenosizegradenodespgrerhormonrfstimestatus
01132490182200018380
121575551203160004031
231140561403300016030
3476945025310401770
451306513025036118550
561642480522110008421
6747548021380002931
789733702029001420
8956967120210015641
9101180450302100010931
Unnamed: 0pidagemenosizegradenodespgrerhormonrfstimestatus
6766771715591242186041305530
677678383651201291260609911
6786791065651171193520009670
679680326430202398045014990
680681124767127241118753112220
6816825865103032115238117600
6826831273641262213561144111520
683684152557135311490209113420
684685736440212316007006290
685686894801727238097217580